Senior Analyst, Agentic AI
The AES Corporation
AzureCloudGoogle Cloud PlatformPythonPyTorchTensorflow
Senior Timing CAD Engineer, Applied AI
NVIDIA
full-time
Posted on:
Location Type: Hybrid
Location: Santa Clara • California, Texas • 🇺🇸 United States
Visit company websiteSalary
💰 $136,000 - $212,750 per year
Job Level
Senior
Tech Stack
PythonPyTorchRayTensorflow
About the role
- Architect and develop AI-driven solutions for static timing, constraints quality, and closure prediction.
- Integrate heterogeneous data sources — timing reports, constraint graphs, design metadata, silicon correlation — into structured knowledge bases and training pipelines.
- Develop autonomous analysis agents that interact with timing tools (e.g., PrimeTime, Nanotime, Tempus) to perform multi-corner, multi-mode optimization and constraint debugging.
- Implement scalable orchestration across Flow-Server and Digital Engineer platforms, enabling AI-in-loop decision-making for sign-off readiness.
- Collaborate with methodology and sign-off teams to validate models on live projects, improving coverage, predictability, and engineering productivity.
- Build interpretable AI pipelines using graph neural networks, large language models, and process-aware reasoning engines for timing closure recommendations.
- Be responsible for the end-to-end lifecycle — from data curation and model training to deployment, monitoring, and continuous improvement in production environments.
Requirements
- BS (or equivalent experience) in Electrical or Computer Engineering with 3 years of experience in AI/ML solution development, ideally for EDA, semiconductor, or complex data domains
- Strong background in VLSI/ASIC design — with deep understanding of timing, constraints, STA, or sign-off workflows.
- Proficiency in Python, PyTorch/TensorFlow, and graph or agentic AI frameworks (e.g., LangGraph, LangChain, Ray, NetworkX).
- Experience developing data pipelines, knowledge graphs, or process models for structured engineering data.
- Working knowledge of timing tools (PrimeTime, Nanotime, Tempus) and scripting integration with EDA environments.
- Experience with AI orchestration frameworks, reasoning based on prompts, and multi-agent automation is highly desirable.
- Strong problem-solving skills, technical depth, and a mentality for experimentation and continuous learning.
- Experience with constraint validation, false-path detection, and timing-exception modeling is a plus.
- Prior exposure to AI in physical design automation, Silicon/process modeling, or EDA flow automation is a plus.
Benefits
- Equity
- Benefits 📊 Resume Score Upload your resume to see if it passes auto-rejection tools used by recruiters Check Resume Score
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AI-driven solutionstiming analysisconstraints qualityclosure predictiondata pipelinesknowledge graphsgraph neural networksPythonPyTorchTensorFlow
Soft skills
problem-solvingtechnical depthexperimentationcontinuous learning
